This study proposes an online deep learning-based channel state estimator for OFDM wireless communication systems by employing the deep learning long short-term memory (LSTM) neural networks. The proposed algorithm is a pilot-assisted estimator type. The proposed estimator is initially offline trained using simulated data sets, and then it follows the channel statistics in an online deployment, where finally the transmitted data can be recovered. A comparative investigation is performed using three different optimisation algorithms for deep learning to evaluate the performance of the proposed estimator at each. The proposed estimator provides a superior performance in comparison to least square (LS) and minimum mean square error (MMSE) estimators when limited pilots are used, thanks to the outstanding learning and generalisation capabilities of deep learning LSTM neural networks. Also, it does not require any prior knowledge of channel statistics. So, the proposed estimator is promising for channel state estimation in OFDM communication systems.

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

Using deep learning technologies, the channel estimate for an orthogonal frequency division multiplexing system (OFDM) based on pilots is done in this work. To be more specific, deep learning gated recurrent unit (GRU) neural networks are used to present a new framework for channel estimation. Initially, it is trained offline using generated data sets, and thereafter it is used online to track the channel parameters, after which the data transmitted can be recovered. For the purpose of determining the performance of the proposed estimator, three alternative deep learning optimization techniques are used to test it. It is also compared to other commonly used estimators, such as least squares (LS) and minimum mean square error (MMSE). In addition, the proposed estimator is compared with two existing models. Deep learning GRU neural network-based channel state estimator, which are capable of learning and generalizing rapidly, are shown to outperform the comparable estimators when just a few pilots are available. In addition, there is no need for prior knowledge of channel statistics. So, estimating OFDM communication system channel states using the proposed estimator appears promising.INDEX TERMS Deep learning, channel estimation, gated recurrent unit, OFDM.

This study proposes novel Long Short-Term Memory (LSTM)-based classifiers through developing the internal structure of LSTM neural networks using 26 state activation functions as alternatives to the traditional hyperbolic tangent (tanh) activation function. The LSTM networks have high performance in solving the vanishing gradient problem that is observed in recurrent neural networks. Performance investigations were carried out utilizing three distinct deep learning optimization algorithms to evaluate the efficiency of the proposed state activation functions-based LSTM classifiers for two different classification tasks. The simulation results demonstrate that the proposed classifiers that use the Modified Elliott, Softsign, Sech, Gaussian, Bitanh1, Bitanh2 and Wave as state activation functions trump the tanh-based LSTM classifiers in terms of classification accuracy. The proposed classifiers are encouraged to be utilized and tested for other classification tasks.INDEX TERMS LSTM, deep neural network, activation Function, tanh gate.

This paper presents a new periodic grooved dielectric leaky-wave antenna with non-identical irregularities for an extremely high-frequency range capable of performing efficiently in the Ka band through a stable gain, a decrease in the level of the side lobes, and the achievement of a narrower main lobe beam. It consists of a dielectric waveguide antenna placed inside a channelled rectangular metallic waveguide to improve performance. A dielectric rod was used to overcome the losses related to the propagation of electromagnetic waves in metal conductors. A grooved dielectric of a 21-element linear array is used to propose a simple approach for the synthesis of such antennas based on a modified energy method and is fed by a standard waveguide. It was designed and simulated at 37.2 GHz for satellite, 5G antenna, and radar applications. The major novelty of this study is the ability to control the direction of the main lobe through non-identical irregularities in geometric parameters such as the width, height, and distance between each element. Radiation techniques have been extensively studied using simulations. A performance antenna with radiation efficiency of 99.35%, gain of 22 dBi, width of radiation pattern of 3.2 $$^{\circ }$$ ∘ , and side lobe level (SLL) of $${-}$$ -  18.3 dB has been achieved.